Safer Prescribing and Care for the Elderly (SPACE): Protocol of a Cluster Randomized Controlled Trial in Primary Care
Bibliographic record
Abstract
BACKGROUND: High-risk prescribing, adverse drug events, and avoidable adverse drug event hospitalizations are common. The single greatest risk factor for high-risk prescribing and adverse drug events is the number of medications a person is taking. More people are living longer and taking more medications for multiple long-term conditions. Most on-going prescribing occurs in primary care. The most effective, cost-effective, and practical approach to safer prescribing in primary care is not yet known. OBJECTIVE: To test the effect of the Safer Prescribing And Care for the Elderly (SPACE) intervention on high-risk prescribing of nonsteroidal anti-inflammatory and antiplatelet medicines, and related adverse drug event hospitalizations. METHODS: This is a protocol of a cluster randomized controlled trial. The clusters will be primary care practices. Data collection and analysis will be at the level of patient. RESULTS: Recruitment started in 2018. Six-month data collection will be in 2018. CONCLUSIONS: This study addresses an important translational gap, testing an intervention designed to prompt medicines review and support safer prescribing in routine primary care practice. TRIAL REGISTRATIONgeneral practice: Australian New Zealand Clinical Trials Registry: ACTRN12618000034235 http://www.ANZCTR.org.au/ACTRN12618000034235.aspx (Archived with Webcite at http://www.webcitation.org/6yj9RImDf)
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.072 | 0.061 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.013 | 0.007 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.069 | 0.013 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".